Pulsating Access Resilience Scheduling Method for Port Three-Dimensional Warehousing System

Abstract

The present invention discloses a pulsating access and resilient scheduling method for a port three-dimensional warehousing system, which is used for a warehousing system with double-depth storage racks, shuttle cars, elevators and a control system. The method includes the steps: the control system issues an outbound task instruction, and the shuttle car receives it according to the first-come, first-served principle; it is judged whether the shuttle car is idle. If it is idle, it travels to the column where the target goods are located, otherwise it waits; after arriving at the target column, it is judged whether to transfer goods. If goods need to be transferred, the blocked goods are first transferred to the vacant storage location, then the target goods are picked up and the elevator is requested. Otherwise, the goods are directly picked up and the elevator is requested; it is judged whether the elevator is idle. If it is idle, it loads the target goods, otherwise it waits; the elevator returns with the goods to the system I / O point for unloading, and the outbound task ends. The goods transfer operation means first moving the blocked goods to the vacant storage location, then picking up the target goods and handing them over to the elevator, and finally transporting the blocked goods back to the original storage location. The present invention can improve the outbound efficiency of the warehousing system, reduce the waiting time, and enhance the resilience and operation stability of the system.

Description

Technical Field

[0001] The present invention relates to a scheduling method, and more specifically to a pulsating access resilience scheduling method for a port three-dimensional storage system. Background Art

[0002] As a new type of intelligent three-dimensional warehouse integrating warehousing management and sorting and distribution functions, the double-depth multi-layer shuttle car storage system has high automation and high space utilization rate, and can realize the rapid access of small-piece, multi-variety and small-batch goods. Therefore, it has received extensive attention from logistics enterprises such as ports and has been applied to the access of bulk goods. However, the quay loading and unloading operations have pulsating characteristics, the ship docking process is complex and delicate, and it takes a long time. The window period for loading and unloading operations after the ship docks is very precious. Therefore, it is necessary to carry out goods transfer operations during the non-loading period to improve the loading efficiency.

[0003] In the storage system of double-depth storage racks, the goods transfer operation will increase the operation distance and time, resulting in an increase in the complexity of operation task scheduling. In addition, in the research on the operation task scheduling of double-depth storage racks, although the goods transfer operation problem is considered, only a rough estimate of the goods transfer operation time is made, the goods transfer operation process is not deeply analyzed, and no corresponding mathematical model is established for the goods transfer operation, which has a certain deviation from the actual automated three-dimensional storage system scheduling problem. Therefore, in-depth research on the goods transfer operation process of the double-depth multi-layer shuttle car storage system and the establishment of an outbound operation model under different goods transfer strategies to optimize the task scheduling of the double-depth multi-layer shuttle car storage system have important theoretical significance and practical value.

[0004] Therefore, there is a prior art invention patent with the application number 202411646109.2 that discloses an intelligent warehousing management method. For a multi-layer shuttle car intensive three-dimensional warehouse, it predicts order information based on historical order data and calculates the goods storage locations and corresponding handling equipment paths of the goods in the inbound orders. It uses an optimization algorithm to calculate the globally optimal goods storage locations, goods handling allocation plans, and handling equipment paths, and performs inbound scheduling on the goods in the inbound orders. However, it fails to carry out goods transfer operations in advance for large-scale tasks to cope with the pulsating impact similar to the ship loading operation tasks.

[0005] The invention patent with the application number 202410969032.6 discloses a four-way shuttle car storage system logistics equipment configuration method, system, and equipment. It establishes an all-instruction completion time model and a logistics equipment unit usage cost model based on the storage system parameter information. It uses the NSGA-II algorithm that combines simulated annealing differential evolution search and problem domain knowledge to solve and configure the four-way shuttle cars and elevators. However, it can only achieve the basic inbound and outbound scheduling of logistics equipment. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a pulsating access resilience scheduling method for a port three-dimensional warehousing system. Based on the actual operation situation, three dumping operation strategies, namely, random point dumping strategy, nearest point dumping strategy and fixed point dumping strategy, are proposed. The dumping operation process is analyzed and an accurate dumping operation time model is established. On the basis of considering the dumping operation, the outbound operation is scheduled and optimized. A double-population genetic algorithm is designed to solve the scheduling model, and a double-population recombination and cooperation optimization strategy is proposed to enable the two populations to evolve differently while exchanging individuals in the populations to break the population state and jump out of the local optimum. In addition, a variable neighborhood search strategy is adopted to increase the diversity of the solution space and improve the search efficiency of the algorithm.

[0007] To achieve the above object, the present invention provides the following technical solutions: A pulsating access resilience scheduling method for a port three-dimensional warehousing system, which is used for a warehousing system with double-depth storage racks, shuttle cars, elevators and a control system, and is characterized in that it includes the following steps:

[0008] Step 1, the control system issues an outbound task instruction, and the shuttle car receives the outbound task instruction according to the first-come-first-served principle.

[0009] Step 2, judge whether the shuttle car is in an idle state. If the shuttle car is in an idle state, the shuttle car travels to the column where the target goods are located. Otherwise, wait for the shuttle car until it is in an idle state.

[0010] Step 3, the shuttle car travels to the column where the target goods are located. Judge whether a dumping operation is required. If so, first perform the dumping operation. After the dumping operation is completed, the shuttle car loads the target goods and requests the elevator to complete the remaining operation process. If the dumping operation is not required, the shuttle car directly loads the target goods and requests the elevator to complete the remaining operation process.

[0011] Step 4, judge whether the elevator is in an idle state. If the elevator is in an idle state, the elevator loads the target goods. Otherwise, wait for the elevator until it is in an idle state.

[0012] Step 5, the elevator loads the target goods and returns to the system I / O point, unloads the target goods, and the outbound task ends.

[0013] Among them, the process of the shuttle car performing the dumping operation is as follows: First, the shuttle car takes out the blocked goods and places them in the remaining empty storage locations, which are also called dumping storage locations. Secondly, the shuttle car then takes out the target goods and hands them over to the elevator to complete the vertical transportation of the target goods. Finally, the shuttle car transports the blocked goods from the dumping storage location to the original storage location.

[0014] As a further improvement of the present invention, the shuttle car dumping operation strategy in the third step is a random dumping strategy, and the specific content of this random dumping strategy is as follows: The control system randomly selects an idle storage location as the dumping location according to the roadway where the target goods are located. The shuttle car first places the blocked goods at the dumping location, then continues to complete the horizontal operation of the outbound task. Finally, the shuttle car returns to this roadway to place the blocked goods at their original positions. At this point, the outbound operation task is completed. The dumping operation time of this random dumping strategy is the running time between the dumping location and the target location, which is calculated by the following formula:

[0015]

[0016] Wherein, is the dumping operation time, is the dumping operation distance, is the average speed of the shuttle car, is the loading / unloading time of the shuttle car, is the shuttle car number, is the number of storage columns per row of the shelf, is the length of a single storage location, g(z) is the probability distribution function of the outbound operation task on the shuttle car number.

[0017] As a further improvement of the present invention, the shuttle car dumping operation strategy in the third step is the nearest point dumping strategy, and the specific content of this nearest point dumping strategy is as follows: The control system selects an idle storage location closest to the target location as the dumping location according to the roadway where the target goods are located. The shuttle car first places the blocked goods at the dumping location, then continues to complete the horizontal operation of the outbound task. Finally, the shuttle car returns to this roadway to place the blocked goods at their original positions. At this point, the outbound operation task is completed; wherein, if there are two idle storage locations with the same distance to the target location, the idle storage location closer to the buffer area is selected as the dumping location. The dumping operation time of this nearest point dumping strategy is calculated by the following formula:

[0018]

[0019] Wherein, is the dumping operation time, is the dumping operation distance, is the average speed of the shuttle car, is the loading / unloading time of the shuttle car, is the length of a single storage location, is the storage location occupancy rate.

[0020] As a further improvement of the present invention, the shuttle unloading operation strategy in step 3 is a fixed-point unloading strategy. Specifically, the control system selects a fixed vacant cargo space as the unloading cargo space according to the lane where the target cargo is located. The shuttle first places the blocked cargo at the unloading cargo space, then continues to complete the horizontal operation of the outbound task. Finally, the shuttle returns to the lane to place the blocked cargo at the original position. At this point, the outbound operation task is completed. The unloading operation time of the fixed-point unloading strategy is calculated by the following formula:

[0021]

[0022] in, For the unloading operation time, is the unloading operation distance, is the average speed of the shuttle, is the shuttle loading / unloading time, Number the lanes. for Fixed unloading location coordinates of the aisle, The coordinates of the target location, The length of a single cargo space.

[0023] As a further improvement of the present invention, the total time of the outbound operation is also included. The calculation steps are as follows: outbound operation tasks, total outbound operation time of the system It can be expressed as:

[0024]

[0025] in, is the number of outbound operation tasks, and Respectively indicate the execution of The running time of the shuttle and elevator during the outbound task, Shuttle loading / unloading time, It is the loading / unloading time of the elevator.

[0026] As a further improvement of the present invention, the shuttle performs the The running time of each outbound task is calculated as follows: The coordinates of the outbound task are , indicating that the outbound task is located at Layer, Lane, No. The shuttle's running time is It consists of three parts: the transfer time of the shuttle vehicle, the loading time, and the goods dumping operation time, which are specifically expressed as follows:

[0027]

[0028] Among them, is the decision factor for the goods dumping operation. When , it indicates that there is a goods dumping operation during the outbound task process; when , it indicates that there is no goods dumping operation during the outbound task process.

[0029] is the goods dumping operation time, is the average speed of the shuttle vehicle, is the loading / unloading time of the shuttle vehicle, is the number of aisle columns, is the length of a single storage location.

[0030] As a further improvement of the present invention, the running time of the elevator when performing the th outbound task is obtained through the following method: For the th outbound task, the elevator operation time consists of the transfer time and the loading time of the elevator,

[0031] as shown in the following formula:

[0032]

[0033] Among them, is the number of storage racks, is the height of a single-layer storage rack, is the average speed of the elevator, is the loading / unloading time of the elevator.

[0034] As a further improvement of the present invention, steps 1 to 5 are executed by constructing a mathematical model. The construction process is as follows: Assign

[0035] outbound tasks to s four-way shuttle vehicles and t elevators for execution. Set the outbound task set as , the four-way shuttle vehicle set as , the elevator set as

[0036] , the task set of the zth shuttle vehicle as , and the outbound operation time as . Establish the following mathematical model:

[0037]

[0038] The constraints are as follows:

[0039]

[0040] Formula (8) indicates that the shuttle performs all outbound tasks; Formula (9) indicates that an outbound task can only be performed by one shuttle; Formula (10) indicates that each shuttle performs at least one outbound task.

[0041] As a further improvement of the present invention, the constructed mathematical model uses a dual-population genetic algorithm to solve the model, and the specific process is as follows:

[0042] Step 1: Use integer coding based on outbound operations to design a three-segment chromosome encoding method, where each chromosome represents a scheduling plan. The first segment is represented by the outbound task number, and the order in which the outbound task number appears indicates the execution order of the outbound task. The second segment is represented by the number of outbound tasks executed by the shuttle car. The third segment is represented by the number of outbound tasks executed by the elevator.

[0043] Step 2: Introduce two populations, denoted as population 1 and population 2, and initialize them using different initialization methods. For population 1, use a random generation method to initialize it according to the constraints. For population 2, first number the outbound tasks and randomly arrange them to obtain segment 1. Then, according to the principle that shuttles and goods are on the same floor, arrange segment 2 to balance some of the operation time. Finally, repeat the generation operation to obtain the initial population.

[0044] Step 3: Define the fitness function, and then select suitable individuals to enter the next generation based on the fitness value calculated by the fitness function;

[0045] Step 4: For both population 1 and population 2, the tournament selection strategy is adopted. The fitness values of two parents are randomly selected for comparison. The one with the higher fitness value is selected to enter the next generation. The operation is repeated to obtain a population of N.

[0046] Step 5, perform adaptive crossover and mutation;

[0047] Step 6: Given the optimization factor , generate a random number, if the random number is less than the given optimization factor , then perform variable neighborhood search on the individual.

[0048] The beneficial effects of the present invention are as follows. In a double-depth multi-tier shuttle car storage system, there is generally a goods transfer operation, which leads to an increase in the inbound and outbound operation time and a decrease in the system operation efficiency. To address this problem, by analyzing the goods transfer operation process, three goods transfer operation strategies, namely, the random point goods transfer strategy, the nearest point goods transfer strategy, and the fixed point goods transfer strategy, are proposed, and an accurate calculation model for the goods transfer operation is established. On this basis, an optimization model for the outbound operation scheduling is established with the goal of minimizing the outbound operation time. A double-population genetic algorithm is designed to solve the model. By introducing variable neighborhood search and double-population recombination and cooperation optimization strategies, the optimization ability of the algorithm is increased, the solution space is effectively expanded, and the search performance of the algorithm is improved. The calculation model for the goods transfer operation can accurately evaluate the goods transfer operation time. In addition, the double-population genetic algorithm has higher optimization efficiency and can effectively shorten the outbound operation time. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] FIG Figure 1 is a schematic diagram of a double-depth multi-tier shuttle car storage system;

[0050] FIG Figure 2 is a schematic diagram of the goods transfer operation process;

[0051] FIG Figure 3 is a flow chart of the outbound operation;

[0052] FIG Figure 4 is a flow chart of the DPGA algorithm;

[0053] FIG Figure 5 is the encoding and decoding process;

[0054] FIG Figure 6 is the crossover operation process;

[0055] FIG Figure 7 are three neighborhood transformation operations;

[0056] FIG Figure 8 is the number of goods transfer operations under three goods transfer strategies;

[0057] FIG Figure 9 is the convergence graph of the comparison algorithm for 60 outbound tasks. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] The method of this embodiment will be further described in detail below in conjunction with the embodiments given in the drawings. The symbolic parameters used in this embodiment are shown in Table 1:

[0059] Table 1 Main Parameter Symbols

[0060]

[0061] The method of this embodiment is mainly used for the existing double-depth multi-tier shuttle car storage system, which is composed of a double-depth storage rack, shuttle cars, elevators, a control system, etc. Refer toFigures 1 to 2 As shown in the figure, ① is the elevator, ② is the shuttle car, ③ is the storage location, ④ is the I / O position, ⑤ is the idle storage location, ⑥ is the buffer area, ⑦ is the target storage location, ⑧ is the blocked storage location, and ⑨ is the transfer storage location. The double-deep storage rack is used for storing goods, and each storage location can store only one piece of goods. The horizontal transportation of goods is realized by the shuttle car. The elevator is installed on the same side of the rack and is used to realize the vertical transportation of goods or the shuttle car. The shuttle car and the elevator can transport only one piece of goods at a time. At the same time, several buffer areas are set in this warehousing system for temporarily storing goods.

[0062] Compared with the warehousing system based on the single-deep storage rack, the double-deep multi-shuttle car warehousing system has a higher space utilization rate. Due to the uniqueness of the system structure, the system may have a transfer operation process during the inbound and outbound operations, which increases the inbound and outbound operation time and thus reduces the system operation efficiency. The transfer operation process is as follows Figure 2 As shown in the figure, when the system issues an instruction to retrieve the target goods (marked as "A") located in the second column, which is blocked by the goods (marked as "B") in the first column at this time. First, the shuttle car retrieves the blocked goods and places them in the remaining idle storage location, which is also called the transfer storage location; secondly, the shuttle car then retrieves the target goods and hands them over to the elevator to complete the vertical transportation of the target goods; finally, the shuttle car transports the blocked goods from the transfer storage location to the original storage location. The distance that the shuttle car travels during the transfer operation is called the transfer distance.

[0063] The inbound and outbound operations of the double-deep multi-shuttle car warehousing system are commanded by the control system, and the shuttle car and the elevator cooperate to complete them. The flowchart of the outbound operation is as follows Figure 3 , and the specific outbound operation process is as follows:

[0064] (1) The system issues an outbound task instruction, and the shuttle car receives the outbound task instruction according to the first-come-first-served principle;

[0065] (2) If the shuttle car is in an idle state, the shuttle car travels to the column where the target goods are located; otherwise, wait for the shuttle car until it is in an idle state;

[0066] (3) The shuttle car judges whether a transfer operation is required. If so, perform the transfer operation first; otherwise, the shuttle car loads the target goods and requests the elevator to complete the remaining operation process;

[0067] (4) If the elevator is in an idle state, the elevator loads the target goods; otherwise, wait for the elevator until it is in an idle state;

[0068] (5) The elevator loads the target goods and returns to the system I / O point, unloads the target goods, and the outbound task ends.

[0069] During the goods rearrangement process of this embodiment, a random rearrangement strategy, a nearest - point rearrangement strategy, and a fixed - point rearrangement strategy are used. The time models of these three rearrangement strategies are as follows:

[0070] Operation time model of the random rearrangement strategy

[0071] The random rearrangement strategy (Rearrangement to a random point, RTRP) means that when a goods rearrangement operation occurs in the system, the control system randomly selects an idle storage location as the rearrangement location according to the aisle where the target goods are located. The shuttle car first places the blocked goods at the rearrangement location, then continues to complete the horizontal operation of the outbound task. Finally, the shuttle car returns to this aisle and places the blocked goods in their original positions. Thus, the goods rearrangement operation time under the random rearrangement strategy is the running time of the shuttle car between the rearrangement location and the target location.

[0072] According to the above description, the goods rearrangement operation time under the random rearrangement strategy is the time consumed by the shuttle car to transfer the blocked goods between the rearrangement location and the target location. Since when the occupancy rate of the storage locations

[0073] is [specific occupancy rate value], all the storage locations in the second column of this warehousing system are occupied, that is, the rearrangement location is in the first column. Therefore, the goods rearrangement operation problem under the random rearrangement strategy can be transformed into an outbound operation problem in a single - depth storage rack. The calculation of the goods rearrangement operation time under this strategy is as follows:

[0074]

[0075] Among them, is the goods rearrangement operation time, is the goods rearrangement operation distance, is the average speed of the shuttle car, is the loading / unloading time of the shuttle car, is the shuttle car number, is the number of storage columns in each row of the rack, is the length of a single storage location, g(z) is the probability distribution function of the outbound operation task on the shuttle car number.

[0076] Operation time model of the nearest - point rearrangement strategy

[0077] The Rearrangement to the nearest point (RTNP) strategy means that when an outbound operation requires an outbound operation, the control system selects an idle cargo location closest to the target cargo location as the outbound cargo location based on the aisle where the target cargo is located. The shuttle car places the blocked cargo at the outbound cargo location first, and then continues to complete the horizontal operation of the outbound operation. Finally, the shuttle car returns to the aisle to place the blocked cargo at its original location, and the outbound operation is completed. If there are two idle cargo locations at the same distance from the target cargo location, the idle cargo location closer to the buffer area is selected as the outbound cargo location. When the cargo location occupancy rate in the system is When the probability of dumping occurs is ( ), the unloading operation time is shown in formula (2):

[0078]

[0079] in, For the unloading operation time, is the unloading operation distance, is the average speed of the shuttle, is the shuttle loading / unloading time, is the length of a single cargo space, is the cargo space occupancy rate.

[0080] Operation time model of fixed-point unloading strategy

[0081] The Rearrangement to the fixed point (RTFP) strategy means that when a delivery task requires a delivery, the control system selects a fixed free location as the delivery location based on the aisle where the target goods are located. The shuttle car places the blocked goods at the delivery location first, then continues to complete the horizontal operation of the delivery task. Finally, the shuttle car returns to the aisle to place the blocked goods at the original location, and the delivery task is completed. The fixed unloading location coordinates of the aisle are ( ), the coordinates of the target location are ( ), the unloading operation time under this strategy can be expressed as:

[0082]

[0083] in, For the unloading operation time, is the unloading operation distance, is the average speed of the shuttle, is the shuttle loading / unloading time, is the roadway number, is the fixed coordinates of the goods dumping location in the roadway, the coordinates of the target goods location, is the length of a single goods location.

[0084] During the operation of the warehousing system, the overall outbound operation time occupies a very important position. Therefore, the method of this embodiment provides a corresponding outbound operation time model, which is specifically as follows:

[0085] Outbound operation time model

[0086] In a double-depth multi-level shuttle car warehousing system, the operation time of the outbound task is determined by the specific number of tasks and is related to the location of the target goods. Through the analysis of the outbound operation process, it can be seen that the operation time of a single outbound task consists of the operation time of the shuttle car, the operation time of the elevator, and the loading time of the equipment. For outbound operation tasks, the total outbound operation time of the system can be expressed as:

[0087]

[0088] Among them, is the number of outbound operation tasks, and respectively represent the running times of the shuttle car and the elevator when executing the th outbound task, the loading / unloading time of the shuttle car, is the loading / unloading time of the elevator.

[0089] Assume that the coordinates of the th outbound task are , indicating that this outbound task is located on the th floor, the th roadway, and the th column. Then the running time of the shuttle car consists of three parts: the shuttle car handling time, the loading time, and the goods dumping operation time, which are specifically expressed as follows:

[0090]

[0091] Among them, is the goods dumping operation decision factor. When , it indicates that there is a goods dumping operation during this outbound task process; when , it indicates that there is no goods dumping operation during this outbound task process. is the goods dumping operation time, is the average speed of the shuttle car, is the shuttle loading / unloading time,

[0092] is the number of lane columns, The length of a single cargo space.

[0093] According to the operation process, for the outbound tasks, elevator operation time It consists of the elevator handling time and loading time. As shown in formula (6):

[0094]

[0095] in, is the number of storage shelves, Single-layer storage shelf height, The average speed of the elevator, It is the loading / unloading time of the elevator.

[0096] In the double-depth multi-layer shuttle warehouse system, through a detailed analysis of the outbound operation process, the outbound operation scheduling problem of the double-depth multi-layer shuttle warehouse system can be described as: The outbound tasks are assigned to s four-way shuttles and t elevators for execution, and the outbound task set is set as , the four-way shuttle set is

[0097] , the elevator set is , the task set of the zth shuttle is , the outbound operation time is In order to better describe the scheduling process of outbound operations, the following mathematical model is established:

[0098]

[0099] The constraints are as follows:

[0100]

[0101] Formula (8) indicates that the shuttle performs all outbound tasks; Formula (9) indicates that an outbound task can only be performed by one shuttle; Formula (10) indicates that each shuttle performs at least one outbound task.

[0102] After obtaining the mathematical model in the above steps, it is necessary to solve the model to guide the subsequent selection of the unloading and scheduling strategies. This example studies the outbound operation scheduling problem in a double-deep, multi-layer shuttle warehouse system. Conventional methods have difficulty finding a suitable solution within the search space. In traditional genetic algorithms, individuals can theoretically exchange information with any other individual. As the algorithm iterates, the information of the most effective individuals quickly spreads globally, facilitating the dissemination of effective information. However, this reduces population diversity and makes the algorithm prone to falling into local optima. In the job scheduling optimization problem based on unloading strategies, it is necessary to both reduce the operation time by adjusting the order of tasks and reduce the unloading time by considering the order of tasks, ultimately minimizing the time required for both inbound and outbound operations. To address the characteristics of this job scheduling optimization problem, a dual population genetic algorithm (DPGA) was designed to solve the model. In view of the characteristics of the problem, a three-segment encoding and corresponding decoding scheme based on job task allocation were designed. The order of job tasks and the random initialization of the population were taken into consideration to reduce the number of unloading operations. At the same time, in order to balance the job task time, a variable neighborhood search strategy was introduced to avoid the algorithm falling into local optimality during the search process, effectively increase the solution space, and improve the algorithm search efficiency.

[0103] DPGA algorithm principle: In order to obtain a better initial solution, a dual population strategy is introduced, and the genetic algorithm is used to perform genetic operations on the initial population to evolve the population. Then, the range of the solution is expanded by the variable neighborhood search strategy to avoid the algorithm falling into the local optimum during the search process. Then, a new population is generated based on the dual population reorganization and collaboration. The algorithm process is shown in the attached figure. Figure 4 shown.

[0104] The basic process of the DPGA algorithm is as follows:

[0105] Step 1: Initialize relevant parameters, mainly including: population size , maximum number of iterations , adaptive adjustment parameters , and optimization factors wait;

[0106] Step 2: Based on the constraints and the same layer rules, generate

[0107] The initial population and ;

[0108] Step 3: Calculate the fitness of each individual in the two populations and sort them. According to the elite retention strategy, select a certain proportion of the best individuals to retain;

[0109] Step 4: Retain the individuals with high fitness in the population according to the tournament selection strategy;

[0110] Step 5: Combine the three - segment coding method and adopt adaptive crossover and mutation operators to perform crossover and mutation operations on the population;

[0111] Step 6: Generate new individuals using the variable neighborhood search strategy and evaluate the new individuals;

[0112] Step 7: Determine whether the current situation meets the algorithm termination condition. If it meets, end the algorithm process and output the optimal solution. Otherwise, perform the dual - population collaborative optimization operation to generate new population 1 and new population 2, and then go to Step 3. Therefore, the solution steps in combination with the above DPGA algorithm are as follows:

[0113] (1) Encoding and decoding

[0114] Adopt integer encoding based on the outbound operation, and design the three - segment chromosome encoding method as shown in the appendix Figure 5 Each chromosome represents a scheduling plan. The first segment is represented by the outbound task number. The position where the outbound task number appears indicates the execution order of the outbound task. The second segment is represented by the number of outbound tasks executed by the shuttle vehicle. The third segment is represented by the number of outbound tasks executed by the elevator.

[0115] When the three - segment encodings are [4,1,5,7,3,2,6], [3,2,2] and [4,3] respectively, after decoding, it can be obtained that the number of tasks executed by shuttle vehicle No. 1 is 3, and the execution order is 4 - 1 - 5; the number of tasks executed by shuttle vehicle No. 2 is 2, and the execution order is 7 - 3; the number of tasks executed by shuttle vehicle No. 3 is 2, and the execution order is 2 - 6; the number of tasks executed by elevator No. 1 is 4, and the execution order is 4 - 1 - 5 - 7; the number of tasks executed by elevator No. 2 is 3, and the execution order is 3 - 2 - 6.

[0116] (2) Population initialization

[0117] The quality of population initialization has a direct impact on the solution results of the genetic algorithm. Introduce a dual - population, denoted as population 1 and population 2, and perform initialization operations on the two populations using different initialization methods. For population 1, according to the constraint conditions, use the random generation method for initialization; for population 2, first randomly arrange the outbound task numbers to obtain segment 1, and then arrange segment 2 according to the principle that the shuttle vehicle and the goods are on the same layer, which can balance a part of the operation time. Finally, repeat the generation operation to obtain the initial population.

[0118] (3) Fitness evaluation

[0119] Fitness evaluation first requires defining a fitness function, and then selecting suitable individuals to enter the next generation based on the fitness values. The fitness function is a statistical method for the fitness values of job scheduling schemes, and it is necessary to ensure that the fitness values are positive. With the minimum job time as the goal, the defined fitness function is used to evaluate the job time, and the reciprocal of the job time is selected as the fitness value of the individual. When the job time is longer, the fitness function value is smaller. The fitness function is shown in Equation (11):

[0120]

[0121] (4)Selection operation

[0122] For both population 1 and population 2, the tournament selection strategy is adopted. Any two parent individuals are selected for fitness value comparison, and the one with the higher fitness value is selected to enter the next generation. By performing this operation in a loop, a population of size N is obtained.

[0123] (5)Adaptive crossover and mutation

[0124] Crossover and mutation are operations to generate new individuals. To avoid the premature convergence and local convergence problems caused by the standard genetic algorithm with fixed crossover and mutation rates, an adaptive crossover and mutation operation method is proposed.

[0125]

[0126] The calculation formulas for the adaptive crossover and mutation probabilities are shown in Equations (12) to (14). is the current iteration number, is the maximum iteration number, is a variable related to the iteration number, is an adaptive adjustment parameter, , is the adaptive crossover probability, is the adaptive mutation probability,

[0127] is a constant value, , and are the average fitness value and the maximum fitness value of the current population respectively, is the larger fitness of the parent individuals during the crossover operation, is the larger fitness value of the parent individuals during the mutation operation.

[0128] Since the three-segment coding method is adopted, a large number of illegal solutions will be generated if the individuals are crossed in the way of the standard genetic algorithm. Therefore, only segment 1 is crossed. As shown in the appendix Figure 6As shown in the figure, the two-point crossover strategy is adopted. Two crossover points are randomly selected from the parental individuals. The task sorting in the gene segment between the two crossover points of Parent 1 is replaced with the corresponding task sorting in Parent 2 to obtain Offspring 1, and the same operation is performed on Parent 2 to obtain Offspring 2.

[0129] The single-point reverse mutation strategy is adopted. A mutation point is randomly selected, and the gene segment after the mutation point is sorted in reverse order. Since the mutation operation will cause a large change in the number of tasks executed by the shuttle car and the elevator, which is not conducive to further generating new individuals, therefore, only the task sequence is mutated.

[0130] (6) Variable neighborhood search

[0131] In the search algorithm, the initial solution generates new solutions through a series of moves. All moves constitute a neighborhood, and the structure of the neighborhood has a great influence on the search performance of the algorithm. In order to avoid the algorithm falling into local optimum during the search process, a variable neighborhood search strategy is designed, which includes three heuristic rules: swap neighborhood, insertion neighborhood, and inversion neighborhood. Given an optimization factor , a random number is generated. If the random number is less than the given optimization factor

[0132] , then variable neighborhood search is performed on the individual. The specific method is as follows:

[0133] Swap neighborhood: Randomly specify two positions in the job task number and the shuttle car task execution number (i.e., Segment 1 and Segment 2) of the individual for swapping to generate a new individual;

[0134] Insertion neighborhood: Randomly select an insertion point in the job task number and the shuttle car task execution number (i.e., Segment 1 and Segment 2) of the individual, and insert the adjacent numbers to obtain a new individual;

[0135] Inversion neighborhood: Randomly select a neighborhood search point in the job task number and the shuttle car task execution number (i.e., Segment 1 and Segment 2) of the individual, and invert the job task number and the shuttle car task execution number after the search point to generate a new individual.

[0136] When solving this job scheduling optimization problem, it is necessary to perform neighborhood search operations on both the job task order and the shuttle car execution order simultaneously. As attached Figure 7As shown in the figure, segment 1 represents the job task sequence, and segment 2 represents the shuttle vehicle execution sequence. By operations such as swapping, inserting, and inverting, a new job scheduling scheme will be generated. Taking the swap neighborhood operation as an example, swapping job tasks [1] and [2] gives a new job sequence [4, 2, 5, 7, 3, 1, 6]; swapping the number of tasks [3] and [2] executed by the shuttle vehicle gives a new allocation scheme [2, 2, 3]. Through variable neighborhood search, both the job task sequence and the number of tasks executed by the shuttle vehicle change, and then a new individual is generated, which is conducive to the algorithm finding a better solution based on the current solution.

[0137] To further improve the quality of the algorithm's solution, a double-population recombination and collaborative optimization strategy is introduced. By exchanging genetic information of the dominant individuals in the two populations, that is, hoping to exchange the dominant solutions of the populations generated by two different methods, the limitation of the one-way evolution of the population can be broken.

[0138] The following examples are provided in this embodiment to further illustrate the advantages of the method in this embodiment:

[0139] Taking the double-depth multi-layer shuttle vehicle storage system of an enterprise as the research object, the outbound operation scheduling is optimized. The storage system has 3 floors, with 3 lanes on each floor, 1 shuttle vehicle configured on each floor, and 3 elevators. The other storage system parameter settings are shown in Table 2. According to the statistical data, the storage system has 90 types of goods, with a monthly shipment volume of about 12,000 pallets. There are 35 outbound tasks to be executed within a certain time window, and the coordinate information of the outbound tasks is shown in Table 3.

[0140] Table 2 Main input parameter settings of the storage system

[0141]

[0142] Table 3 Coordinate points of outbound tasks

[0143]

[0144] To verify the impact of the three goods transfer strategies of RTRP, RTNP, and RTFP on the outbound operation, when the occupancy rate of the storage location is 0.8, the above 35 outbound tasks are executed respectively. The selection of control parameters is shown in Table 4. In addition, a comparison is made with the system operation scheduling under the existing method for estimating the goods transfer operation time. For the RTFP goods transfer strategy, the decision maker selects the middle position of each lane as the fixed goods transfer location.

[0145] Table 4 Algorithm parameter settings

[0146]

[0147] According to the program operation, the execution order and task completion time of the outbound operation under the three stock-transfer strategies of RTRP, RTNP, and RTFP are obtained respectively, as shown in Table 5. Under the RTRP strategy, the task execution orders of the three shuttles are [20, 11, 29, 23, 17, 5, 9, 27, 3, 15, 31, 26, 14, 33], [7, 16, 13, 18, 22, 35, 1, 21, 30, 4, 10, 25], and [19, 6, 12, 32, 24, 2, 28, 8, 34], and the completion time is 558.25 s; under the RTNP strategy, the task execution orders of the three shuttles are [5, 9, 11, 29, 26, 21, 20, 3, 15, 27, 14, 33, 17, 31], [18, 22, 13, 1, 23, 30, 7, 16, 21, 10, 35, 4], and [32, 2, 8, 19, 6, 12, 34, 24, 28], and the completion time is 493.68 s; under the RTFP strategy, the task execution orders of the three shuttles are [29, 5, 9, 35, 26, 3, 11, 15, 12, 27, 20, 33, 23, 17], [21, 30, 7, 1, 31, 21, 10, 22, 18, 13, 16, 4], and [14, 28, 2, 34, 8, 32, 6, 19, 24], and the completion time is 520.83 s. The task execution order of the shuttle operation under the stock-transfer operation time estimation strategy is the same as that of the RTFP strategy, and the operation time is 562.46 s. The task execution orders of the RTFP strategy and the estimation strategy are the same, but the operation times are different. Under the estimation strategy, the decision maker adopts the fixed-point stock-transfer strategy based on experience and makes an approximate estimation of the stock-transfer operation time each time, without precise calculation, so an error is generated. As the number of stock-transfer operations in the system increases, the estimation error also gradually increases, indicating that the derived calculation method of the stock-transfer operation time can accurately and effectively calculate the stock-transfer operation time, thereby reducing the error caused by estimation and realizing the accurate evaluation of the system operation time considering the stock-transfer operation.

[0148] Table 5 Comparison of outbound operation under three stock-transfer strategies

[0149]

[0150] The occupancy rate of storage locations is an important factor affecting the optimization of the outbound task scheduling of this system. The higher the occupancy rate of storage locations, the more goods are stored in the system, and the greater the probability of stock-transfer operations during the outbound task process. To compare the impacts of the three stock-transfer strategies of RTRP, RTNP, and RTFP on the outbound operation under different occupancy rates of storage locations, the above 35 outbound tasks are executed under the conditions of occupancy rates of storage locations being 0.6, 0.7, 0.8, and 0.9 respectively.

[0151] As shown in Table 6, through simulation calculations, the transfer operation time and task completion time of the three transfer operation strategies of RTRP, RTNP, and RTFP are obtained respectively under different occupancy rates of storage locations. With the increase of the occupancy rate of storage locations, the transfer operation time and task completion time of the three transfer strategies of RTRP, RTNP, and RTFP all increase. The larger the occupancy rate of storage locations, the fewer idle storage locations in the warehousing system, the more complex the distribution of outbound tasks, and the greater the probability of transfer operations during the outbound task process, resulting in an increase in both the transfer operation time and the task completion time. In addition, with the increase of the occupancy rate of storage locations, the transfer operation time of the RTFP strategy gradually becomes less than that of the RTRP strategy. Since the idle storage locations continuously store goods, the probability of finding idle storage locations under the RTRP strategy gradually decreases, and the transfer operation time gradually increases, while the RTFP strategy is not affected by this condition. Under different occupancy rates of storage locations, the transfer operation time and task completion time under the RTNP strategy are the smallest, because during the transfer operation process, the transfer distance of the shuttle vehicle is the smallest, indicating that the RTNP strategy is more suitable for the actual operation situation.

[0152] Table 6 Outbound operation conditions under different transfer strategies

[0153]

[0154] Under different occupancy rates of storage locations, the number of transfer operations under the three transfer strategies of RTRP, RTNP, and RTFP is as shown in the appendix Figure 8 As shown. When the occupancy rate of storage locations is small, the difference in the number of transfer operations under the three transfer strategies is not significant, because when the occupancy rate of storage locations is low, the number of transfer operations during the outbound task process is small; with the increase of the occupancy rate of storage locations, the goods stored in the warehousing system increase, and the number of transfer operations will also increase. When the occupancy rate of storage locations exceeds 0.75, the number of transfer operations under the RTRP strategy is the largest, and the number of transfer operations under the RTNP strategy is the smallest. Because when the occupancy rate of storage locations increases, the difficulty of determining the transfer storage location under the RTRP strategy increases, while it is easier to determine the transfer storage location under the RTNP strategy. According to the above results, when the occupancy rate of storage locations is small, the RTRP, RTNP, and RTFP strategies can be selected arbitrarily. When the occupancy rate of storage locations exceeds 0.7, it is optimal to adopt the RTNP strategy.

[0155] To verify the effectiveness of the proposed variable neighborhood search and double-population recombination and cooperation optimization strategies, two groups of comparative experiments were designed, and the experiments were repeated 50 times for the cases where the scale of the outbound task was 35, 60, 80, and 100 respectively. The experimental results are shown in Table 7, where the DPGA-1 algorithm represents the use of only the variable neighborhood search strategy, and the DPGA-2 algorithm represents the use of only the double-population recombination and cooperation optimization strategy. According to the result comparison, it can be seen that under different task scales, the minimum completion time and average completion time of the DPGA algorithm are better than those of the DPGA-1 algorithm, indicating that this strategy can improve the optimization ability of the algorithm, thus verifying the effectiveness of the double-population recombination and cooperation optimization strategy; by comparing the results of the DPGA algorithm and the DPGA-2 algorithm, it can be seen that the use of the variable neighborhood search strategy can effectively increase the solution space and avoid the algorithm falling into local optimum, verifying the effectiveness of the variable neighborhood search strategy.

[0156] Table 7 Comparative experimental results for verifying the effectiveness of variable neighborhood search and population cooperation

[0157]

[0158] To further verify the effectiveness of the algorithm, the algorithm was experimentally compared with the improved grey wolf optimization algorithm (MGWO) and the genetic-beam search hybrid optimization algorithm (GA-BS) for the outbound operation scheduling, and the experiments were repeated 50 times for the cases where the scale of the outbound operation task was 35, 60, 80, and 100 respectively.

[0159] Table 8 Comparison of the results of three algorithms

[0160]

[0161] The experimental comparison results of the three algorithms are shown in Table 8. Under different task scales, the solution accuracy and stability of the algorithm are better than those of the two comparative algorithms, indicating that the proposed algorithm is an effective solution algorithm. As the task scale increases, the outbound operation task scheduling becomes more complex. Compared with the mGWO and GA-BS algorithms, the solution accuracy and stability advantages of the proposed algorithm continue to expand, and the optimization effect is more obvious. When the task scale ranges from 35 to 100, the optimization efficiency of the DPGA algorithm increases from 13.28% to 24.26%, indicating that the algorithm is more suitable for solving large-scale scheduling optimization problems. Attachment Figure 9 Figure shows the convergence comparison diagram of the three algorithms under 60 outbound tasks. The mGWO algorithm can converge faster, but it is easy to fall into local optimum, resulting in low solution accuracy. Compared with the mGWO algorithm, the GA-BS algorithm has a slower convergence speed in the early stage, but the search results are better; According to the attachment Figure 9It can be seen that within 400 iterations, each algorithm can converge, and the proposed algorithm has the highest initial convergence speed and solution accuracy. Thus, compared with the mGWO and GA-BS algorithms, the proposed algorithm has stronger optimal solution solving ability at the cost of a certain amount of computing time.

[0162] In summary, it is first verified that the proposed calculation method for the goods transfer operation time can accurately and effectively calculate the goods transfer operation time, reducing the error caused by estimation. Secondly, the results show that when the occupancy rate of the storage location is small, any of the three goods transfer strategies can be selected; when the occupancy rate of the storage location exceeds 0.7, the RTNP strategy is more suitable for the actual operation situation. Finally, through comparative experiments, the superiority of the proposed variable neighborhood search strategy and double-population cooperation optimization strategy is verified. The use of double-population cooperation optimization can strengthen the information exchange between populations, improve the optimization ability of the algorithm, while the variable neighborhood search strategy can effectively expand the solution space and enhance the global search ability of the algorithm.

[0163] In summary, for the resilient scheduling method of pulsating access in the port three-dimensional warehousing system of this embodiment, three goods transfer operation strategies, namely the random point goods transfer strategy, the nearest point goods transfer strategy, and the fixed point goods transfer strategy, are proposed based on the actual operation situation. The goods transfer operation process is analyzed and an accurate goods transfer operation time model is established. An accurate goods transfer operation calculation model and an outbound operation scheduling optimization model are established. On the basis of considering the goods transfer operation, the outbound operation is scheduled and optimized. A double-population genetic algorithm is designed to solve the scheduling model, and a double-population recombination and cooperation optimization strategy is proposed to enable the two populations to evolve differently while exchanging individuals in the populations to break the population state and jump out of the local optimum. In addition, the variable neighborhood search strategy is adopted to increase the diversity of the solution space and improve the search efficiency of the algorithm.

[0164] The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A pulsating access and resilient scheduling method for a port three-dimensional storage system, which is used for a storage system with double-depth storage racks, shuttle cars, elevators and a control system, and is characterized in that: It includes the following steps: Step 1, the control system issues an outbound task instruction, and the shuttle vehicle receives the outbound task instruction according to the first-come, first-served principle; Step 2, determine whether the shuttle vehicle is in an idle state. If the shuttle vehicle is in an idle state, the shuttle vehicle travels to the column where the target goods are located. Otherwise, wait for the shuttle vehicle until it is in an idle state; Step 3, the shuttle vehicle travels to the column where the target goods are located, and determine whether a goods transfer operation is required. If so, first perform the goods transfer operation. After the goods transfer operation is completed, the shuttle vehicle loads the target goods and requests the elevator to complete the remaining operation process. If the goods transfer operation is not required, the shuttle vehicle directly loads the target goods and requests the elevator to complete the remaining operation process; Step 4, determine whether the elevator is in an idle state. If the elevator is in an idle state, the elevator loads the target goods. Otherwise, wait for the elevator until it is in an idle state; Step 5, the elevator loads the target goods and returns to the system I / O point, unloads the target goods, and the outbound task ends; Among them, the process of the shuttle vehicle performing the goods transfer operation is as follows: First, the shuttle vehicle takes out the blocked goods and places them in the remaining empty storage location, which is also called the goods transfer storage location; Second, the shuttle vehicle then takes out the target goods and hands them over to the elevator to complete the vertical transportation of the target goods; Finally, the shuttle vehicle transports the blocked goods from the goods transfer storage location to the original storage location; The goods transfer operation strategy of the shuttle vehicle in Step 3 is the random goods transfer strategy, and the specific content of this random goods transfer strategy is: The control system randomly selects an empty storage location in the roadway where the target goods are located as the goods transfer storage location. The shuttle vehicle first places the blocked goods in the goods transfer storage location, and then continues to complete the horizontal operation of this outbound task. Finally, the shuttle vehicle returns to this roadway and places the blocked goods in the original position. At this point, this outbound operation task is completed. The goods transfer operation time of this random goods transfer strategy is the running time of the shuttle vehicle between the goods transfer storage location and the target storage location, and it is calculated through the following formula: ； Among them, is the time for goods transfer operation, is the distance of goods transfer operation, is the average speed of the shuttle vehicle, is the loading / unloading time of the shuttle vehicle, is the shuttle vehicle number, is the number of storage columns per row of shelves, is the length of a single storage location, g(z) is the probability distribution function of the outbound operation task on the shuttle vehicle number.

2. The pulsating access resilience scheduling method for the port three-dimensional warehousing system according to claim 1, characterized in that: The goods transfer operation strategy of the shuttle vehicle in Step 3 is the nearest-point goods transfer strategy, and the specific content of this nearest-point goods transfer strategy is: The control system selects an empty storage location closest to the target storage location in the roadway where the target goods are located as the goods transfer storage location. The shuttle vehicle first places the blocked goods in the goods transfer storage location, and then continues to complete the horizontal operation of this outbound task. Finally, the shuttle vehicle returns to this roadway and places the blocked goods in the original position. At this point, this outbound operation task is completed; Among them, if there are two empty storage locations with the same distance from the target storage location, select the empty storage location closer to the buffer area as the goods transfer storage location. The goods transfer operation time of this nearest-point goods transfer strategy is calculated through the following formula: ； Among them, is the time for goods transfer operation, is the distance of goods transfer operation, is the average speed of the shuttle vehicle, is the loading / unloading time of the shuttle vehicle, is the length of a single storage location, is the occupancy rate of the storage location.

3. The pulsating access resilience scheduling method for the port three-dimensional warehousing system according to claim 1, characterized in that: The operation strategy of the shuttle vehicle for unloading goods in Step 3 is the fixed-point unloading strategy, and the specific fixed-point unloading strategy is as follows: The control system selects a fixed empty storage location as the unloading storage location according to the roadway where the target goods are located. The shuttle vehicle first places the blocked goods at the unloading storage location, then continues to complete the horizontal operation of the outbound task. Finally, the shuttle vehicle returns to this roadway again to place the blocked goods at the original position. Thus, the outbound operation task is completed. The unloading operation time of the fixed-point unloading strategy is calculated by the following formula: ； Among them, is the time of goods transfer operation, is the distance of goods transfer operation, is the average speed of the shuttle car, is the loading / unloading time of the shuttle car, is the roadway number, is the fixed goods transfer location coordinates of the roadway, the coordinates of the target location, is the length of a single location.

4. The resilient scheduling method for pulsating access of the port three-dimensional warehousing system according to any one of claims 1 to 3, characterized in that: It also includes the total time of the outbound operation Calculation steps, specifically obtained by calculating through the following formula. For outbound operation tasks, the total system outbound operation time can be expressed as: ； Among them, is the number of outbound operation tasks, and respectively represent the running times of the shuttle car and the elevator when executing the th outbound task, the loading / unloading time of the shuttle car, is the loading / unloading time of the elevator.

5. The pulsating access resilience scheduling method for the port three-dimensional storage system according to claim 4, characterized in that: The running time of the shuttle when executing the th outbound task is obtained by the following method: Assume that the th outbound task coordinate is , indicating that the outbound task is located on the th floor, the th roadway, and the th column. Then the running time of the shuttle consists of three parts: the handling time of the shuttle, the loading time, and the goods dumping operation time, which are specifically expressed as follows: ； Among them, is the decision factor for the goods transfer operation. When , it indicates that there is a goods transfer operation during the outbound task process; when , it indicates that there is no goods transfer operation during the outbound task process; is the goods transfer operation time, is the average speed of the shuttle vehicle, is the loading / unloading time of the shuttle vehicle, is the number of aisle columns, is the length of a single storage location.

6. The pulsating access resilience scheduling method for the port three-dimensional warehousing system according to claim 5, wherein: The running time of the elevator when performing the th outbound task is obtained by the following method: For the th outbound task, the operating time of the elevator consists of the elevator handling time and the loading time, as shown in the following formula: ； Among them, is the number of storage shelves, is the height of a single-layer storage shelf, is the average speed of the elevator, is the loading / unloading time of the elevator.

7. The resilient scheduling method for pulsating access of the port three-dimensional warehousing system according to any one of claims 1 to 4, characterized in that: Steps 1 to 5 are executed by constructing a mathematical model, and the construction process is as follows: Assign outbound tasks to s four-way shuttles and t elevators for execution. Set the outbound task set as , the four-way shuttle set as , the elevator set as , the task set of the z-th shuttle as , the outbound operation time as , and establish the following mathematical model: ； The constraint conditions are as follows: ； ； ； Equation (8) represents that the shuttle vehicle executes all outbound tasks; Equation (9) represents that one outbound task can only be executed by one shuttle vehicle; Equation (10) represents that each shuttle vehicle executes at least one outbound task.

8. The pulsating access resilience scheduling method for the port three-dimensional storage system according to claim 7, characterized in that: The constructed mathematical model uses the double-population genetic algorithm to solve the model, and the specific process is as follows: Step 1: Adopt integer coding based on the outbound operation, design a three-segment chromosome coding method, and each chromosome represents a scheduling scheme; the first segment is represented by the outbound task number, and the position where the outbound task number appears indicates the execution order of the outbound task; the second segment is represented by the number of outbound tasks executed by the shuttle vehicle; the third segment is represented by the number of outbound tasks executed by the elevator. Step 2: Introduce two populations, denoted as Population 1 and Population 2, and perform initialization operations on the two populations using different initialization methods; for Population 1, according to the constraint conditions, use the random generation method for initialization; for Population 2, first randomly arrange the outbound task numbers to obtain Segment 1, then arrange Segment 2 according to the principle that the shuttle vehicle and the goods are on the same layer to balance part of the operation time, and finally repeat the generation operation to obtain the initial population. Step 3: Define the fitness function, and then select suitable individuals to enter the next generation according to the fitness values obtained by calculating the fitness function. Step 4: For both Population 1 and Population 2, adopt the tournament selection strategy, randomly select 2 parent generations to compare the fitness values, and select the one with the higher fitness value to enter the next generation. Repeat the operation to obtain a population with a quantity of N. Step 5: Perform adaptive crossover and mutation. Step 6, given the optimization factor , generate a random number. If the random number is less than the given optimization factor , then perform variable neighborhood search on the individual.

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